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Studentized continuous wavelet transform (t-CWT) in the analysis of individual ERPs: real and simulated EEG data

机译:在单个ERP分析中的学生化连续小波变换(t-CWT):真实和模拟的EEG数据

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摘要

This study aimed at evaluating the performance of the Studentized Continuous Wavelet Transform (t-CWT) as a method for the extraction and assessment of event-related brain potentials (ERP) in data from a single subject. Sensitivity, specificity, positive (PPV) and negative predictive values (NPV) of the t-CWT were assessed and compared to a variety of competing procedures using simulated EEG data at six low signal-to-noise ratios. Results show that the t-CWT combines high sensitivity and specificity with favorable PPV and NPV. Applying the t-CWT to authentic EEG data obtained from 14 healthy participants confirmed its high sensitivity. The t-CWT may thus be well suited for the assessment of weak ERPs in single-subject settings.
机译:这项研究旨在评估学生连续小波变换(t-CWT)的性能,该方法是一种提取和评估单个受试者数据中与事件相关的脑电势(ERP)的方法。评估了t-CWT的敏感性,特异性,阳性(PPV)和阴性预测值(NPV),并使用六个低信噪比的模拟EEG数据与各种竞争程序进行了比较。结果表明,t-CWT将高灵敏度和特异性与良好的PPV和NPV相结合。将t-CWT应用于从14位健康受试者获得的真实EEG数据中,证实了其高敏感性。因此,t-CWT可能非常适合在单对象环境中评估较弱的ERP。

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